RNA-Seq gene expression estimation with read mapping uncertainty

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ID: 296484
2009
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Abstract
Abstract Motivation: RNA-Seq is a promising new technology for accurately measuring gene expression levels. Expression estimation with RNA-Seq requires the mapping of relatively short sequencing reads to a reference genome or transcript set. Because reads are generally shorter than transcripts from which they are derived, a single read may map to multiple genes and isoforms, complicating expression analyses. Previous computational methods either discard reads that map to multiple locations or allocate them to genes heuristically. Results: We present a generative statistical model and associated inference methods that handle read mapping uncertainty in a principled manner. Through simulations parameterized by real RNA-Seq data, we show that our method is more accurate than previous methods. Our improved accuracy is the result of handling read mapping uncertainty with a statistical model and the estimation of gene expression levels as the sum of isoform expression levels. Unlike previous methods, our method is capable of modeling non-uniform read distributions. Simulations with our method indicate that a read length of 20–25 bases is optimal for gene-level expression estimation from mouse and maize RNA-Seq data when sequencing throughput is fixed. Availability: An initial C++ implementation of our method that was used for the results presented in this article is available at http://deweylab.biostat.wisc.edu/rsem. Contact: cdewey@biostat.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics on
Reference Key
openalex_W2107564978 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bo Li, Victor Ruotti, Ron Stewart, James A. Thomson, Colin N. Dewey
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Journal BMC Bioinformatics
Year 2009
DOI
10.1093/bioinformatics/btp692
URL
https://academic.oup.com/bioinformatics/article-pdf/26/4/493/48855011/bioinformatics_26_4_493.pdf
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